EXECUTIVE SUMMARY

There are indications of a housing affordability problem in
the United States.

As in the past, exclusionary zoning appears to be having a
significant negative effect on housing affordability. There appears, however,
to be a greater emerging threat. The rapid adoption of exclusionary planning
policies, through smart growth, already appears to be severely impacting
affordability and has great potential to do much more to make housing less
affordable. At the same time, smart growth does not appear to have compensating
benefits for eligible recipients of housing assistance or for housing
assistance programs in general.

This report reviews broad economic indicators of housing
affordability and the impact of exclusionary policies on housing affordability
(exclusionary zoning and smart growth).

The findings are summarized below (Table ES-1).

Indicators of Housing Affordability

Finding 2.1: Lowest quintile incomes continue to rise
at a slower rate than average, but the rate of increase has improved
substantially in recent years.

Historically, incomes of the lowest quintile
households tend to rise at a rate less than average. By far the strongest
lowest quintile income increases in recent years have been registered since the
enactment of welfare reform, as income levels for the lowest quintile rose at
more than double the rate of any similar period since 1980.

Finding 2.2: The actual demand for housing subsidies
is not known due to discrepancies among federal income and expenditure reporting
systems.

Generally, households that must
spend more than 30 percent of their income on rent are eligible for federal
housing assistance. But, because there are widely varying indicators of income,
the extent of the housing assistance need cannot be definitively known. The
Bureau of Labor Statistics (BLS) Consumer Expenditure Survey indicates
that lowest income quintile households spend 2.3 times their income and that
expenditures exceed income in quintiles two and three. The Bureau of the
Census, based upon the Current Population Survey (CPS), estimates lowest
quintile incomes somewhat higher, but still well below the expenditure level
reported by BLS (expenditures are 1.7 times CPS income). It seems implausible
that low-income households are spending 1.7 times their income every year.

Most housing assistance demand estimates use CPS
figures. If, for example, the BLS expenditure estimate is a more accurate
indicator of average household income, then the extent of the housing
affordability problem would be considerably less.

Finding 2.3: Home ownership is generally increasing,
and increasing most rapidly among minority households.

During the 1990s, the nation enjoyed the most
widespread gains in home ownership since the 1950s, and now stands at a record
level. At the same time, minority home ownership has been rising at three times
the rate of White-Non-Hispanics.

Finding 2.4: Owner occupied housing affordability has
declined somewhat over the past decade. However, housing affordability has
dropped significantly in some states and metropolitan areas.

House values rose 20 percent relative to income in
the 1990s. In some states and metropolitan areas, affordability increased
substantially. However, in others there was a serious decline. The least affordable
areas are all in California, the Boston area, the New York metropolitan area
and Portland, Oregon, where the median income household cannot afford more than
one-half of the homes.

Finding 2.5: Rents have remained comparatively
constant in relation to low-income household income in the last decade.

There is some variation in the experience with
rental costs relative to income. Some measures indicate slight declines in
affordability, while others indicate slight improvements. Most measures,
however, indicate that a slight improvement in affordability in the last five
years.

Finding 2.6: There are indications of a shortage of
affordable housing units, especially in particular geographical areas.

Rental vacancy rates have fallen slightly at the
national level over the past decade. However, there have been sharp drops in
vacancy rates in a number of metropolitan areas. Vacancy rates are especially
low in California and in the New York and Boston metropolitan areas, the same
areas that exhibit some of the most severe owner occupant housing affordability
problems.

Finding 2.7: The indicators outlined above do not
indicate a significant nation-wide housing affordability problem. However,
there are indications of serious problems in some areas.

The broad indicators of affordability indicate a
somewhat mixed situation. Incomes are rising and rents are generally stable.
Moreover, it is possible that, due to income reporting discrepancies, the
extent of unmet housing assistance need may be less than previously estimated.
On the other hand, vacancy rates have fallen significantly in some areas,
likely indicating a shortage of rental units, while housing affordability has
remains low in some areas and has declined sharply in others.

Barriers to Housing Affordability

Exclusionary zoning and growth controls were cited in the
early 1990s Kemp Commission report as significant barriers to housing
affordability. Exclusionary zoning remains so, but growth controls, in the form
of so-called “smart growth” policies that ration development and land, have
emerged as a more serious threat, due to their broad and rapid adoption.

Smart growth has arisen as a reaction to urban sprawl, the
spatial expansion of US urban areas that has occurred since World War II, as
urban populations have increased (and urban population densities have
declined). What is not understood by many US observers, however, is that urban
sprawl is occurring virtually everywhere that affluence is rising, and that the
relative rate of sprawl (density reduction) is actually greater in Europe,
Asia, Canada and Australia, than it has been in the United States.

Exclusionary zoning, the practice of limiting entry
into local housing markets by lower income and particular ethnic populations
continues to be a barrier to housing affordability. This can be accomplished by
requiring lower densities than the market would produce or even by outrightly
prohibiting low-income housing such as apartment units. One frequently
occurring practice is the prohibition on lower cost housing types, such as
manufactured housing and modular housing. Some of the most notable exclusionary
zoning problems are in the Boston and New York metropolitan areas, which are
among the nation’s least affordable markets.

Many communities have implemented development impact
fees, which are assessed on new single family and multiple unit residences to
finance new infrastructure. This practice has replaced reliance on general
taxation and bonding, which was the historical approach to infrastructure
finance. While there are arguments for making development “pay for itself,”
this particular strategy has increased the cost of housing in areas where it is
used. A University of Chicago study found that, in the Chicago area,
development impact fees increased the cost of all housing, not just the cost of
new housing. In the San Francisco Bay area, development impact fees reach nearly
$65,000 per new owner occupied unit, and more than $40,000 for rental units. In
one community development impact fees are equal to $0.62 per $1.00 of rental
unit construction value. Development impact fees ration both owner occupied and
multiple unit housing, thereby raising prices and impairing affordability. The
impact on affordable housing is regressive, since development impact fees are
the same, regardless of the value of unit being constructed.

Consistent with economic theory, rationing land,
especially through the smart growth exclusionary planning strategy of urban
growth boundaries, increases housing costs and reduces affordability. Because
lower income households are more financially vulnerable, they shoulder a
disproportionately greater share of the burden.

A number of areas have adopted “smart growth”
strategies that ration the amount of land available for development. Examples
are urban growth boundaries, down zoning, and other strategies that
artificially reduce the amount of land available for development. This has had
the effect of reducing competition, thereby increasing the cost of the factors
of production, limiting housing supply and reducing affordability. A case in
point is the Portland (Oregon) area, where the National Association of
Homebuilders Housing Opportunity Index has declined 44.5 percent (percentage of
homes in the area affordable to the median income household) in the last 10
years. Portland had by far the steepest affordability drop among major
metropolitan areas. Similarly, Bureau of the Census data indicates that Oregon,
with its statewide exclusionary planning (smart growth) laws, led the nation
from 1990 to 2000 in both housing value escalation and the increase of housing
values relative to incomes (both by a wide margin). The upward cost pressures
of land rationing on the single family housing market also tend to increase
rents, increasing housing burdens for both recipients of housing assistance and
those eligible for whom there is insufficient public funding for finance.

Lower overall home ownership rates and lower Black
home ownership rates are associated with areas more consistent with the higher
densities that smart growth requires.

A fundamental requirement of smart growth is higher
population densities. Yet, higher population densities are associated with
lower levels of home ownership. Recent research also indicates that Black home
ownership is lower and Black dwelling unit size is smaller in areas with higher
population densities. The higher costs that are associated with smart growth
have the potential to increase the number of households eligible for housing
assistance, to make it more costly to serve present recipients, and, as a
result, to reduce the number of households that can be served.

Lower overall household expenditures are associated
with metropolitan areas that sprawl more, which benefits all income classes and
makes it possible to serve more households with housing assistance.

As would be expected, expenditures for
transportation are higher in areas that sprawl more. But the lower housing
costs in the more sprawling areas more than compensate for the transportation
cost differential. Food costs are also lower where there is more sprawl. The
higher costs associated with smart growth have the potential to increase the
number of household eligible for housing assistance, to make it more costly to
serve present recipients, and, as a result, to reduce the number of households
that can be served.

Finding 3.26: Smart growth is associated with greater
traffic congestion, longer commute times and more intense air pollution.

Contrary to popular perception, traffic congestion
and air pollution are less intense in areas that sprawl more. This is indicated
by both the US and international evidence.

Transit is generally slower than the automobile;
even where high levels of transit are available. As a result, journey to work
travel times are less in more sprawling areas, including for low-income
workers.

Similarly, the hope urban areas might be redeveloped
to better match jobs and residences, leading to a fundamental change in travel
patterns, is unrealistic. Fundamentally, the transportation demand reducing
objective of “walkability,” “transit-oriented development” and “mixed-use”
urban designs is likely to have no more than marginal impacts. Modern urban
areas are large employment and shopping markets. The compartmentalization that
these schools of urban design would require is simply at odds with how people
choose to live, work and shop. In the modern urban area, people often choose to
work or shop at areas that are not particularly close to where they live. The
same is true of low-income households. It makes little sense to expect that
changes in the urban form can bring jobs and shopping closer to people when
people seem disinclined to shop or work at the closest locations today.

Even if there were a broad commitment to the
required and significant land use changes, the conversion process would take many
decades for material change to occur, and a serious vision of the changes that
would be required and how they would be achieved has not been articulated. In
the much more dense and more transit-oriented urban areas of Europe that might
be looked to as models, virtually all growth in recent decades has been in the
suburbs, which rely principally on the automobile. The political and economic
reality is that there is no prospect for redesigning urban areas in a manner
that materially improves employment mobility opportunities for eligible
recipients assistance in the near future. Further, the often tax-supported
trend toward infill development in central cities could displace low-income
households, forcing them to move to areas farther from employment and transit
service.

Low-income employees have work trips that are
similar in duration to that of all commuters and are only marginally more
highly represented among workers traveling more than one-hour each way to work.

Low-income households are most likely to achieve
their employment potential if their geographical labor market is larger, rather
than smaller. The automobile generally provides access to the largest possible
labor market.

The lowest income households that are eligible for
housing assistance have generally less access to automobiles than other
households. For decades, the overwhelming majority of new jobs have been created
outside the urban cores. On average, 90 percent of urban jobs are now outside
downtown areas. Generally, these jobs are simply not accessible by transit in a
reasonable travel time (if at all) to the overwhelming majority of residential
locations in the urban area.

Because of slower transit speeds, the labor market
available to the average automobile commuter is approximately five times the
area available to the average transit commuter. The most important objective
for improving low-income access to larger labor markets is to increase
automobile availability.

The high cost of transit makes it
impossible to provide the comparatively rapid mobility throughout a large urban
area that is available by car. The political and economic reality is that
financing present levels of transit service is a challenge in many metropolitan
areas and implementation of the transit service levels that would bring a
material improvement for eligible recipients is inconceivable. It makes more
sense to improved income mobility by encouraging automobile ownership than to
vainly seek reformation of an urban form toward the end of bringing jobs and
shopping to low income people.

Finding 3.28: Because it is not feasible to negate
its affordability destroying impacts, smart growth works at cross-purposes to
the nation's housing assistance programs.

Even today, the nation does not remotely provide the
funding level that would be required if all households eligible for housing
assistance in fact received housing assistance. Moreover, there seems to be no
short-term likelihood that substantially greater funding will be provided.
Smart growth imposes affordability losses across the income spectrum, not just
on low-income households. It is not feasible to design housing subsidy programs
that would compensate in any systematic or comprehensive way for the housing
affordability loss generated by smart growth. At whatever level of public
expenditure, exclusionary planning must reduce the number of households for
which housing assistance can be afforded.

Widespread adoption of exclusionary planning is
likely to reduce home ownership levels and could reverse the substantial
progress toward the national goal of greater home ownership. This burden will
fall most on lower income households, which are disproportionately ethnic
minorities. Thus, an indirect impact of exclusionary planning could be to
reverse progress toward another national goal, integrating minority households
into the economic mainstream. Smart growth could render the present home
ownership level unsustainable, much less additional progress.

The inevitable affordability destroying impacts of
smart growth (exclusionary planning) are at their root inconsistent with
policies that would seek to ensure adequate shelter for all.

Exclusionary planning is likely to drive development
from areas that have adopted smart growth to areas that have not. It could even
result in the rise of informal, substandard housing communities outside the
highly regulated areas, and induce further sprawl and driving. Finally, smart
growth could result in the emergence of two classes of metropolitan areas ---
the more elite that adopt the exclusionary planning policies that artificially
raise housing prices and the less elite, which do not.

It might be
argued that the consequences of smart growth’s exclusionary planning would be
acceptable if there were more than compensating benefits. But smart growth does
not appear to produce benefits that compensate for its apparent destruction of
housing affordability. Where there is less sprawl (where urban development is
more consistent with smart growth policies):

·Home ownership rates are lower.

·Low-income household home ownership rates are lower.

·Black home ownership rates are disproportionately
lower.

·Cost of living expenditures are higher.

·Work trips take longer

·Traffic congestion is greater

·Air pollution is more intense

These are not outcomes that improve the quality of
life, whether for the population in general or eligible recipients of housing
assistance in particular. The rapid adoption of smart growth, because of its
inconsistency with economic dynamics, is likely to significantly reduce housing
affordability.

Policy Options:

Based upon the analysis above, the following policy options
are suggested to encourage improved housing affordability:

Income Estimation:

·The U.S. Department of Commerce, the U.S. Department of
Labor and the U.S. Department of Housing and Urban Development could establish
a process for determining the cause of these disparate estimates and propose
methods by which accurate and consistent data can be developed and routinely
reported by both reporting systems.

·Once the more accurate system is in place, US
Department of Housing and Urban Development could prepare an estimate of the
number of households eligible for housing assistance.

Exclusionary Planning (Smart Growth) and
Exclusionary Zoning

·The Secretary of Housing and Urban Development could
recommend to the President the issuance of an executive order reaffirming the
fundamental commitment of the U.S. Government to continued home ownership
expansion and housing opportunities for all. The order could review the
progress toward increasing home ownership among the population in general and
with respect to minorities in particular. The executive order should, within
the constraints of applicable law, forbid the use federal funding by federal
departments and agencies for programs that promote smart growth policies that
would ration land or development (such as urban growth boundaries or
development impact fees) and are thereby likely to reduce housing
affordability.

·The U.S. Department of Housing and Urban Development
could publish an Urban Development and Housing Affordability Guide Book for
local communities on the negative impacts of regulatory barriers to housing
affordability, with particular emphasis on the impacts of exclusionary zoning
and smart growth’s exclusionary planning policies. The Urban Development and
Housing Affordability Guide Book could include information with respect to the
quality of life impacts of smart growth policies for eligible recipients of
housing assistance.

·The U.S. Department of Housing and Urban Development
could prohibit the use of research and technical assistance funding for the
support of projects and programs that contribute to the problem of housing
affordability, such as exclusionary zoning, and exclusionary planning (land
rationing and development impact fees)

·The U.S. Department of Housing and Urban Development
could establish and maintain a comprehensive, locality specific database of
regulatory barriers such as urban growth boundaries, other land rationing
initiatives, development impact fees (including amounts) and any other such
provisions inconsistent with the established economic principle that rationing
leads to higher prices and reduced housing affordability. Once such a database
is developed, the US Department of Housing and Urban Development could produce
an annual report on progress toward removing regulatory barriers to
affordability and develop policy options (actual federal and models for states
and localities) to encourage removal of barriers to affordability.

Table ES-1

Findings

Section

Finding

2.1

Lowest quintile incomes continue to rise at a slower rate
than average, but the rate of increase has improved substantially in recent
years.

2.2

The actual demand for housing subsidies is not known due
to discrepancies among federal income and expenditure reporting systems.

2.3

Home ownership is generally increasing, and increasing
most rapidly among minority households.

2.4

Owner occupied housing affordability has declined somewhat
over the past decade. However, housing affordability has dropped
significantly in some states and metropolitan areas.

2.5

Rents have remained comparatively constant in relation to
low-income household income in the last decade.

2.6

There are indications of a shortage of affordable housing
units, especially in particular geographical areas.

2.7

The indicators outlined above do not indicate a
significant nation-wide housing affordability problem. However, there are
indications of serious problems in some areas.

3.1

As noted in the Kemp Commission report, exclusionary
zoning continues to limit housing.

Housing affordability is measured by the relationship
between income and the cost of housing. Improving housing affordability,
therefore, requires increasing incomes relative to housing costs or reducing
housing costs relative to incomes. From a policy perspective, this requires
measures that encourage the lowest feasible housing costs (competitive costs)
and/or sufficiently high incomes, which are generally associated with higher
levels of employment. Thus, policies options that reduce housing costs increase
affordability, while policies that increase incomes increase affordability.

Governments in the United States provide housing assistance
to low-income households. But there is a limit the amount of funding that
public processes will make available for housing subsidies. In the long run,
housing affordability will be more sustainable if the market produces housing
at a low enough cost for the largest number of households to afford at market
determined incomes. Again, as in the case of welfare, such a policy goal is
more likely to be achieved if employment levels among recipients of housing
assistance are higher.

For decades, public policy in the United States has favored
home ownership. In response, home ownership is now at its highest recorded
level, 67.4 percent.[1]
But there are threats to continued progress and even indications that housing
affordability could decline in the future. Affordability losses not only make
it more difficult for low income households to live in decent accommodations,
but it also reduces their ultimate potential to achieve home ownership and the
greater affluence with which it is associated.

However, there is evidence of a housing affordability crisis
in the United States.

·The United States Department of Housing and Urban
Development (HUD) has found that affordable housing units have declined over
the past decade and that the decline accelerated from 1997 to 1999.[2]

·In some metropolitan areas, the price of single-family
dwellings has risen so much that even middle-income households find it
difficult to afford homes, such as in the San Francisco Bay area.

·In the early 1990s, the Kemp Commission identified
various barriers to housing affordability. These barriers continue to interfere
with housing affordability today.[3]

This paper reviews the housing affordability situation in
the United States using broad economic indicators and reviews the impact of
exclusionary policies on affordability, especially smart growth.

1.1
HOUSING ASSISTANCE

Generally, households are eligible for federal housing
assistance if their housing expense (rent plus utilities other than telephone)
exceeds 30 percent of income. However, housing assistance funding is
considerably below the amount that would be required to assist all eligible
recipients. In 1999, the General Accounting Office estimated that more than
two-thirds of eligible households do not receive housing assistance (Table 1).[4]
As a result, households that are eligible are placed upon waiting lists,
sometimes for years, before they can obtain housing assistance. Thus, based
upon the current definition of eligibility, housing assistance is rationed.

Among eligible households that do not receive assistance,
more than one-half are considered “worst case needs,” by virtue of rent[5]
expense that exceeds 50 percent of household income. Another 30 percent of
unassisted households have rent expense between 30 percent and 50 percent of
income.

During the 1990s, incomes generally rose among lower income
households. From 1990 to 1995, average incomes rose 0.3 percent annually in the
lowest income quintile, and in the latter one-half of the decade average incomes
rose 1.7 percent annually (2000$).[6]
This 1995 to 2000 increase rate was by far the highest in the last 20 years for
the lowest income quintile (Table 2).[7]
Virtually all of the increase in the last five years occurred since welfare
reform was enacted (1996). Moreover, lowest quintile income rose 10.3 from 1990
to 2000, more than the 9.6 percent increase in overall median income. The
impact, however, of the present economic downturn is not yet known.

Finding: Lowest quintile incomes continue to rise at
a slower rate than average, but the rate of increase has improved substantially
in recent years.

There is some question as to the actual extent of need for
housing assistance. There are material discrepancies between income and related
data reported by federal estimation systems (Figure 1).

The
Bureau of Labor Statistics Consumer Expenditures Survey” estimated
that the 1999 average income of the lowest income quintile of households
was $7,264.[8]

The
Census Bureau estimated average lowest quintile income at $9,940, based
upon the Current Population Survey (CPS), 37 percent above the Consumer
Expenditures Survey figure.[9]
This source is generally used by HUD for income estimates.

But the Consumer Expenditure Survey indicates a much
higher level of expenditures than income for households in the lowest quintile.
In 1999, average expenditures, including tax payments, were $16,913.

·Compared to the Bureau of Labor Statistics income
estimate, lowest quintile households spent 2.33 times their income. If this is
an accurate estimation of income, then lowest quintile households spent, on
average, $9,649 more than their income in 1999.

·Compared to the CPS income estimate, lowest quintile
households spent 1.70 times their income. If this is an accurate estimation of
income, then lowest quintile households spent, on average, $6,973 more than
their income in 1999.

Moreover, BLS also estimates income at less than
expenditures for households in Quintiles 2 and 3. Households in Quintile 2 have
an annual deficit of more than $7,000, while households in Quintile 3 have an
annual deficit of nearly $3,000. Even the higher CPS income estimate is lower
than expenditures for Quintile 2, by approximately $850.

The discrepancies between income and expenditures has been
evident for some time. In 1989, the CPS income estimate for the lowest Quintile
was $6,900 below expenditures, nearly duplicating the 1999 relationship. The
BLS income estimate was $8,700 below the expenditure estimate, slightly below
the 1989 amount.[10]

It does not seem plausible that the lowest 40 to 60 percent
of American households spend more than they receive in income. Further, it
seems even more doubtful that households in the nation’s lowest income quintile
spend from 70 to 133 percent more than they receive, year in and year out.
These discrepancies could result from under-reporting of income, over-reporting
of expenditures or some combination of the two.

It would thus seem that, if the expenditure estimates from
the Consumer Expenditure Survey are representative, they are also more
reasonable approximations of actual income for the quintiles in which
expenditures are reported to exceed income.

There are other indications that there may be income under-reporting
in the CPS data. Research by Rector, Johnson and Youssef indicated that that
1996 Census Bureau personal income estimates were approximately 30 percent
below estimates in the National Income and Product Accounts system in 1996.[11]
Further, they found an under-reporting of more than $500 billion in government
cash transfer payments to individuals in the CPS income estimate. In the same
year, the amount by which expenditures exceeded income in the BLS data for the
bottom three quintiles was approximately $425 billion.[12]

Under-reporting of income by housing assistance recipients
has received the attention of the HUD Inspector General. In 2000, the Inspector
General estimated housing assistance overpayments in the amount of $935 million
as a result of under-reporting income:

Tenants often do not report
income or under-report income which, if not detected, causes HUD to make
excessive subsidy payments.[13]

This potential income under-reporting is significant with
respect to assessing the extent of need for housing assistance programs. This
is illustrated by examining data from Lincoln, Nebraska, which in 1999 had per
capita income approximately equal to the national average (Table 3).[14]
Comparing the national lowest quintile data to HUD fair market rents for the
Lincoln area yields the following:

The fair market rent on a
two-bedroom apartment in the Lincoln area would require 86.7 percent of the BLS
Quintile 1 average income

The fair market rent on a
two-bedroom apartment in the Lincoln area would require 63.4 percent of the
Census Bureau Quintile 1 average income

The fair market rent on a
two-bedroom apartment in the Lincoln area would be 37.2 percent of the BLS
Quintile 1 average expenditures for 1999. This is less than one-half the BLS
figure and 40 percent below the Census Bureau figure.

Table
3

Various
Income Estimation Methods:

Example
of Lincoln, NE, 1999

BASED UPON ESTIMATED MEDIAN

This is based upon Lincoln, NE

Fair Market Rent

$6,300

Income: BLS

$7,264

Fair Market Rent Share

86.7%

Income: Census

$9,940

Fair Market Rent Share

63.4%

Expenditures

$16,913

Fair Market Rent Share

37.2%

Source: HUD and US Department
of Commerce, Bureau of Economic Analysis.

Finding:The actual demand for housing subsidies is
not known due to discrepancies among federal income and expenditure reporting
systems.

National policy has sought to expand home ownership over the
past 50 years. Homeownership yields significant external benefits. Home
ownership is important to the nation’s wealth creation. Home equity was found
to be the greatest source of household wealth in a 1995 HUD Urban Policy
Brief. [15] This in and
of itself would seem to justify policies that favor home ownership.

Home equity is the largest element of the average
household’s wealth.[16]
Home equity can be used to finance college education, or new business startups.
Denying home ownership to a significant percentage of citizens could have far
reaching social implications.

The Policy Brief also cited evidence that
neighborhoods with higher home ownership levels tend to be more stable. The
characteristic most associated with the “American Dream” is home ownership.
Indeed, the Kemp Commission suggested that home ownership had become the
“Universal Dream”[17]

Home ownership reached a record 67.4 percent in 2001. The
highest rate was in the Midwest, at 72.6 (2000) percent, followed by the South
at 69.6 percent. The Northeast trailed at 63.5 percent, and the West was lowest
at 61.8 percent (Table 4).[18]

During the 1990s, home ownership rose 5.4 percent, which
according to Fannie Mae is the most widespread increase since the 1950s.[19]The highest rates of increase were in the
Midwest, at 7.5 percent and the West at 6.4 percent. Home ownership increased
5.8 percent in the South, but increased only 1.4 percent in the Northeast.[20]

The increase in home ownership extended to low income
households as well. Data in the Consumer Expenditures Survey indicates
that home ownership in the lowest income quintile rose from 41 percent in 1989
to 43 percent in 1999, which at 4.9 percent was somewhat below the national
increase of 5.4 percent (Table 5).

Given its wealth producing characteristics, home ownership
is principal means by which lower income minorities enter the economic
mainstream. The greatest home ownership gains are now being achieved by Blacks
and Hispanics, which virtually tripled the rate of increase of White-Non
Hispanics over the last 10 years (Table 6). However, overall rates of minority
home ownership continue to lag significantly, with both Black and Hispanic
rates more than 35 percent below that of White Non-Hispanics.

Finding: Home ownership is generally increasing, and
increasing most rapidly among minority households.

Table
4

Home
Ownership Rates by Region

National

Northeast

Midwest

South

West

1970

65.2%

60.5%

69.6%

68.3%

59.7%

1980

65.6%

60.8%

69.8%

68.7%

60.0%

1990

64.0%

62.6%

67.6%

65.8%

58.1%

2000

67.4%

63.5%

72.6%

69.6%

61.8%

Change from 1970

3.3%

4.8%

4.3%

1.9%

3.4%

CHANGE BY DECADE

1970-1980

0.5%

0.4%

0.2%

0.7%

0.5%

1980-1990

-2.5%

3.0%

-3.2%

-4.3%

-3.3%

1990-2000

5.4%

1.4%

7.5%

5.8%

6.4%

Source: US Census Bureau

Table
5

Home
Ownership in Lowest Income Quintile: 1989-1999

Year

Home

Ownership
%

1989

41%

1994

40%

1999

43%

Change 1989-1999

4.9%

Source: US Department of Labor
BLS Consumer Expenditure Survey

Table
6

Home
Ownership Rates by Ethnicity

Race/Ethnicity

1991

2001

Change

All

64.0%

67.7%

5.7%

White Non-Hispanic

69.5%

74.2%

6.7%

Black

42.7%

48.5%

13.6%

Hispanic

39.0%

46.4%

19.1%

Source: US Census Bureau,
Current Population Survey, March 2001.

2.4
HOUSE VALUES

This increase in home ownership came despite a significant
increase in median home values. From 1990 to 2000, US median home values rose
19.6 percent (Table E-1[21]).[22]
In 2000, the median house value was $120,500, compared to $100,800 in 1990, up
19.6 percent.

Housing was most affordable in West Virginia, Arkansas,
Oklahoma, Mississippi and North Dakota, where median values were $75,000 or
less. The least affordable states were Hawaii, California, Massachusetts, New
Jersey and Washington, where median values were $169,000 or higher (Table E-2).

House values fell in 11 states, with the largest losses in
Connecticut, Rhode Island, New Hampshire, New Jersey and California (Table
E-3), ranging from minus 13.4 percent (California) to minus 26.3 percent
(Connecticut).

The largest increases in median home values were in Oregon,
Utah, Colorado, Michigan and South Dakota, ranging from 42.2 percent in South
Dakota to 74.6 percent in Oregon.

House Prices and Affordability:
One measure of affordability is the ratio between median household income
and median house value. On average, median household income was 0.350 of the
median house value in 2000.This
represents an affordability loss of 8.3 percent from 1990, when the income to
house value ratio was 0.381.There was, however, considerable variation by state
(Table E-4).

Relative to income, this measure indicates that houses are
most affordable in Iowa, where the income to house value ratio in 2000 was
0.535. The least affordable state was Hawaii, with an income to house value
ratio of 1.67 (Table E-5),

Affordability improved the most in Connecticut, Rhode
Island, Maine, California and New Jersey, ranging from Connecticut where the
income to house value ratio rose 36.9 percent. Affordability by this measure
declined the most in Oregon, at minus 35.4 percent (Table E-6).

Metropolitan Areas: Similarly, housing affordability
and trends have varied widely at the metropolitan level. The National
Association of Homebuilders Housing Opportunity Index (HOI) measures the
percentage of homes that can be afforded by the median income family in
metropolitan areas (Table E-7).[23]

The most affordable metropolitan areas are now
Dayton-Springfield, Indianapolis, Kansas City, Syracuse and Harrisburg. In each
of these metropolitan areas (and Youngstown, Ohio), the median income family
can afford more than 80 percent of the homes in the area. All of the five least
affordable metropolitan areas are in California, with San Francisco the lowest,
where the median income family can afford only 6.7 percent of houses. Nearby
Oakland, San Jose and Stockton are also among the least affordable metropolitan
areas, as also is San Diego (Table E-8). All major metropolitan areas in which
the median income family cannot afford more than one-half of homes are in
California, the Boston and New York metropolitan areas and Portland, Oregon.

Housing affordability improved in 58 of the 83 metropolitan
areas. The greatest increases in affordability occurred in Ventura-Oxnard,
Honolulu, Los Angeles, New York and New Haven, all registering above 100
percent. The greatest reductions in affordability occurred in Portland, San
Francisco, Denver, Detroit and San Jose, ranging from a loss of 44.5 percent in
Portland to 17.0 percent in Ann Arbor (Table E-9).

Finding: Owner occupied housing affordability has
declined somewhat over the past decade. However, housing affordability has
dropped significantly in some states and metropolitan areas.

2.5
RENTS

Generally, where single-family housing prices are higher,
apartment rents tend to also be higher. Analysis of American Housing Survey
metropolitan area data indicates that median rents are generally higher where
housing prices are higher. During the 1990 to 2000 period, rents tended to
increase at nearly $20 per month for each $10,000 increase in median house
value or $96 for each $50,000 increase.[24]

Over the past 10 years, average rents have declined slightly
in the United States (inflation adjusted). The 1.2 percent decline is in
contrast to the 19.6 percent increase in average house value (Table 7). During
the period, rents peaked in 1993 at 6.7 percent above the 1989 rate, but have
since fallen to 0.8 percent below 1989.

While the current level of
rent is burdensome for households eligible for housing assistance, the
situation appears to have eased somewhat in the last decade.

The average national rent dropped
8.6 percent relative to the income of the lowest income quintile, from 60.9
percent to 55.7 percent. At the mid-point of the decade (1994), the national
average rent rose to 67.0 percent, but dropped to 1999. The mid-point rise was
the result of falling real incomes and rising rents (Table 8).

“Out-of-pocket” rent[25]
dropped 0.8 percent relative to the expenditures of the lowest income quintile,
from 47.2 percent to 46.7 percent.[26]
At the mid-point of the decade (1994), the national average rent rose to 50.9
percent, but dropped to 1999 (Table 8).

Table
7

Average
Rent: 1990-2000

United
States

Year

Average Rent

Change

1990

$489

0.0%

1991

$503

2.9%

1992

$504

3.1%

1993

$512

4.7%

1994

$498

1.8%

1995

$495

1.2%

1996

$487

-0.4%

1997

$474

-3.1%

1998

$487

-0.4%

1999

$476

-2.7%

2000

$483

-1.2%

Inflation Adjusted

Source: US Census Bureau

Table
8

CPS
Income Estimates and Rent:

Lowest
Income Quintile

COMPARED TO NATIONAL AVERAGE
RENT

Year

Average
Income

Average
Rent

Rent/Income

1989

$9,160

$5,578

60.9%

1994

$8,644

$5,788

67.0%

1999

$9,940

$5,532

55.7%

Change

8.5%

-0.8%

-8.6%

COMPARED TO LOWEST QUINTILE
RENT

Year

Average
Income

Lowest
Quintile Shelter Rent

Rent/Income

1989

$9,160

$4,327

47.2%

1994

$8,644

$4,396

50.9%

1999

$9,940

$4,642

46.7%

Change

8.5%

7.3%

-1.1%

Sources: Calculated from US
Census Bureau and BLS data.

As was noted above, it is also possible that the Consumer
Expenditure Survey expenditures figure may represent a more accurate
approximation of income in income quintiles where expenditures are reported to
exceed income.

·The average national rent declined from 34.3 percent of
lowest income quintile expenditures in 1989 to 33.0 percent in 1999 (Table 9).

·The average “out-of-pocket” rent[27]
for lowest income quintile households increased from 26.6 percent in 1989 to
27.7 percent of income in 1999 (Table 9).

Table
9

BLS
Expenditure Estimates and Rent:

Lowest
Income Quintile

COMPARED TO NATIONAL AVERAGE
RENT

Year

Expenditures

Average
Rent

Rent/Expenditures

1989

$16,283

$5,578

34.3%

1994

$16,140

$5,788

35.9%

1999

$16,750

$5,532

33.0%

Change

2.9%

-0.8%

-3.6%

COMPARED TO LOWEST QUINTILE
RENT

Year

Expenditures

Lowest
Quintile Shelter Rent

Rent/Expenditures

1989

$16,283

$4,327

26.6%

1994

$16,140

$4,396

27.2%

1999

$16,750

$4,642

27.7%

Change

2.9%

7.3%

4.3%

Sources: Calculated from US Census Bureau and BLS data.

These improving trends are confirmed by the latest HUD Worst Case Needs Report. From 1997 to
1999 the number of worst case needs households (households in which rents
exceed 50 percent of income) declined 440,000, a drop of eight percent. This
represents a reversal of the trend of the previous decade.[28]
HUD found that the principal reason for the improvement was rising incomes
among worst case needs households.

Finding: Rents have remained comparatively constant
in relation to low-income household income in the last decade.

At the same time, rental vacancies remained comparatively constant.
From 1990 to 2000, overall rental unit vacancies increased from 7.4 percent to
8.0 percent. The largest increase occurred in single units. At the same time,
vacancies in buildings with multiple units have fallen from in the range of
four to five percent (Table 10).

Table
10

Vacancy
Rates: 1990-2000

Year

All
Rental Units

Single
Unit

2
& Over Units

5
& Over Units

1990

7.4%

4.0%

9.0%

9.6%

1991

7.2%

3.9%

9.4%

10.4%

1992

7.4%

3.8%

9.4%

10.0%

1993

7.3%

3.7%

9.4%

10.2%

1994

7.4%

4.5%

9.1%

9.8%

1995

7.6%

5.4%

9.0%

9.5%

1996

7.9%

5.5%

9.2%

9.6%

1997

7.8%

5.8%

9.0%

9.1%

1998

7.9%

6.3%

9.0%

9.4%

1999

8.1%

7.3%

8.7%

8.9%

2000

8.0%

7.1%

8.6%

9.1%

8.1%

77.5%

-4.4%

-5.2%

Source: US Census Bureau

The national data, however, masks marked regional differences
(Table E-10). In 1990, the nation’s lowest multi-unit vacancy rates were
slightly below five percent (4.7 percent in Wisconsin and 4.9 percent in New
York). By 2000, seven states had vacancy rates below five percent (Table E-11),
and three had fallen below four percent (Massachusetts, New Hampshire[29]
and California).

The 2000 Census data indicates that the lowest vacancies are
disproportionately concentrated in the San Francisco, Boston, Los Angeles and
New York metropolitan areas (Table E-12). These metropolitan areas and other
California metropolitan areas comprise two-thirds of the 41 markets in which
vacancy rates are below 4.0 percent. Other major metropolitan areas at below
4.0 percent vacancy rates are Minneapolis-St. Paul and Austin. In addition,
eight smaller metropolitan areas with large universities have vacancy rates
below 4.0 percent.[30]

In Boston, one of the nation’s least affordable areas, the
governor of Massachusetts has noted that construction of multiple unit
residences has fallen by more than one-half in relation to all housing
construction during the 1990s. Moreover, Governor Swift noted that the rate of
multiple unit development in Massachusetts was trailing the national rate by
two-thirds.[31]

It appears likely that higher immigration has resulted in
much higher demand for rental housing in some urban areas, which may have been
a major contributor to the lower vacancy rates in those areas (Appendix A).

Further, there are indications that the supply of affordable
rental units is declining. HUD reports that, from 1997 to 1999, there was a
loss of 13 percent in housing units affordable to extremely low-income
households.[32] By far the
most significant problem was in the West, where there were just 59 affordable
units for every 100 extremely low-income households,[33]
well below the national average of 79. The Northeast (77), Midwest (84) and
South (92) had higher ratios of affordable housing for every 100 extremely
low-income households.

Finding: There are indications of a shortage of affordable
housing units, especially in particular geographical areas.

2.7 HOUSING AFFORDABILITY: ASSESSMENT

The broad indicators of affordability indicate a somewhat
mixed situation. Incomes are rising and rents are generally stable and it is
possible that, due to income reporting difficulties, the extent of unmet
housing assistance need may be less than previously estimated. On the other
hand, vacancy rates have fallen significantly in some areas, likely indicating
a shortage of rental units. Housing affordability is low in some areas and has
declined sharply in others.

Finding: The indicators outlined above do not
indicate a significant nation-wide housing affordability problem. However,
there are indications of serious problems in some areas.

3.0 BARRIERS TO HOUSING AFFORDABILITY

In 1991, the “Kemp Commission,”[34]
issued a seminal report on barriers to affordable housing. Its report, Not
in My Back Yard, identified a number of factors that were, taken together,
working to reduce the affordability of housing. The most important barriers
were “excessive and unnecessary” regulatory barriers, often arising from
resistance in neighborhoods to housing that would be less expensive.

Two regulatory barriers identified by the Kemp Commission
continue to ration affordable housing.

Exclusionary
Zoning: Zoning has long been used with the effect of keeping out
unwanted land uses, income classes and even ethnic groups. A principal
justification for zoning is the perceived interest of owners to preserve
and enhance the value of their property.The use of zoning for such purposes is referred to as “exclusionary
zoning.” Exclusionary zoning remains a serious impediment to housing
affordability.

Smart
Growth: The use of regional or metropolitan growth controls has
expanded significantly as more communities adopt so-called “smart growth”
policies that ration the land available (especially urban growth
boundaries) or exactions (such as development impact fees or “proffers”).
The impact of the smart growth rationing strategies is similar to that of
exclusionary zoning, though on a broader regional than local or
neighborhood basis. Lower income households (and because of their
disproportionate representation, especially minority households) are
excluded from home ownership and encounter rental housing affordability
problems. Smart growth’s land and development rationing strategies might
therefore be characterized as “exclusionary planning” by virtue of its
implementation through the regional or metropolitan planning process[35]
Smart growth exclusionary planning strategies have become very popular
among urban planners and governments, and may therefore represent the most
significant threat to housing affordability.

That these two factors continue to weaken affordability is
indicated by a recent National Low Income Housing Coalition report (Out of
Reach 2001), which found that all of the 10 least affordable metropolitan
and county/local[36] rental
markets were in areas that have been identified with exclusionary zoning or
exclusionary planning difficulties (below).[37]
This section examines the impact of both exclusionary zoning and smart growth’s
exclusionary planning.

The history of zoning in the United States is complex and
there are arguments both for and against the practice. Zoning is a strategy for
excluding various types of development. This might be what are considered
incompatible commercial uses in residential areas, or, as has often been the
case, developments that house certain income classes or ethnic groups. In the
final analysis, zoning provides incumbent owners extra-territorial jurisdiction
over the property of others.

Exclusionary zoning was identified by the Kemp Commission as
one of the most important regulatory barriers to affordable housing.
Exclusionary zoning is the use of local zoning powers to exclude types of
housing development that are considered undesirable. Exclusionary zoning has
been directed at keeping low-income households out of communities and
neighborhoods, by restricting or even banning the more affordable types of
housing, such as rental units, manufactured housing or modular housing. There
is also evidence that exclusionary zoning has been used to keep particular
types of households out of neighborhoods or communities, especially minority
households.[38]

Recently, a number of areas in growing metropolitan areas
have sought to control growth through the use of the exclusionary zoning
strategy of “down-zoning.” This exclusionary zoning strategy involves reducing
the number of residences that can be built on a particular sized lot. This has
the impact of raising costs by raising both the cost of land prices and
infrastructure for single-family dwellings. Downzoning also makes it very
difficult to build the multiple unit buildings that are relied upon to such a
great degree by recipients eligible for housing assistance. Downzoning has been
particularly popular in suburban areas of northern Virginia, adjacent to
Washington, DC.

The Boston metropolitan area has one of the nation’s most
intense housing affordability problems. Governor Swift’s report (above)[39]
attributes much of the cause to exclusionary zoning strategies that include
overly large lot size requirements, provisions that make development more
difficult or slow, and absolute prohibitions on multiple unit construction. In
most communities, new housing must be developed at lower densities than the
housing stock that already exists. These strategies often arise from a concern
among municipalities that the public service cost of new residences in the
community will exceed the tax revenue received to support the new services.

Areas in which serious exclusionary zoning difficulties have
been reported are well represented in the Out of Reach 2001 list of 10
least affordable areas.[40]
This includes:

Two
metropolitan areas (Boston and New York).[41]
The other two metropolitan areas with sectors in the least affordable 10
have extensively employed smart growth exclusionary planning (below).

Six
municipalities, all in the New York area. The other four municipalities
and counties are in the San Francisco area, which uses exclusionary
planning strategies.

In recent years, considerable public policy attention has
been given to the issue of urban sprawl. While definitions of urban sprawl are
elusive,[42] generally
urban sprawl is associated with lower or declining urban densities. American
urban areas have historically been the world’s least dense (Figure 2). However,
since 1960, urban densities have fallen at a faster rate in virtually all other
developed areas of the world (Figure 3), as urban sprawl has been generally
associated with rising incomes around the world. Even the most dense urban
areas of Europe have sprawled significantly (Appendix D).

At the same time, central cities throughout the developed
world have lost population at their cores. In many central cities, this loss
has been masked by annexation or consolidation with suburbs.[43]
But where annexations and consolidations have generally not occurred, the
population loss trend is evident. Among the 60 such high-income nation central
cities that had achieved 500,000 population and were fully developed by 1950,
only one (San Francisco) is at its population peak. Population and population
density has declined in 59 of the 60 central cities.[44]
All urban areas outside the United States for which data is available had lower
densities in 1990 than in 1960.[45]
A number of low density US urban areas have increased their densities over the
same period of time, though remain far below European and Asian densities.[46]Further, US urban areas have been under much
greater population pressure than their counterparts in Europe. Since 1950, US
population growth has been at a rate more than three times that of the European
Union.[47]
Approximately 90 percent of that US population growth has been urban, rather
than rural.[48]

Various concerns have given rise to anti-sprawl strategies,
which are also referred to as “smart growth,” and “growth management.” Examples
of smart growth strategies are:

Promoting
higher urban population densities.

Preserving
open space and agricultural land

More
reliance on transit and discouragement of driving and highway construction

Greater
mixed-use development (commercial and residential together) and a better
spatial balance between employment and residences.

Rationing
of land for development, through urban growth boundaries and other
strategies that place large tracts of land “off limits.”

Financial
strategies that place virtually the entire burden for new infrastructure
on new development, abandoning historic policies that distributed the
burden more widely.

The key to smart growth and anti-sprawl strategies is higher
population densities. To achieve the goals of smart growth, such as reducing
the use of automobiles, and reducing the amount of land under development
requires future development to be at higher density than has typically been the
case in recent decades

Two smart growth policies can be classified as “exclusionary
planning,” by virtue of the fact that they exclude households, especially lower
income and disproportionately minority households, from the housing market by
artificially raising prices. Exclusionary planning policies include land
rationing (such as urban growth boundaries) and development rationing (through
development impact fees). The rationale for smart growth rests on a number of
arguments related to the environment and quality of life. These rationales,
however, are not without dispute (Appendix B).

Areas in which extensive exclusionary planning is used are
also in the Out of Reach 2001 list of 10 least affordable areas.[49]
This includes:

Two
metropolitan areas (San Francisco and Los Angeles).[50]
The other two metropolitan areas with sectors in the least affordable 10
have extensive use of exclusionary zoning (above).

Four
counties, all in the San Francisco area. The other six municipalities and
counties are in the New York area, which uses exclusionary zoning
strategies.

3.22 EXCLUSIONARY PLANNING: DEVELOPMENT RATIONING

Until comparatively recently, it has been the custom for US
local governments to pay for infrastructure such as city streets, water systems
and wastewater systems with general funds or bond proceeds.

This began to change, however, with the passage of
Proposition 13 in California (1978), which limited property taxes. Property tax
rates were capped at one percent of valuation and annual increases were limited
to two percent. This resulted in an immediate reduction of property tax
revenues, but additional state aid was quickly made available to compensate for
the loss. In fact, total per capita property taxes and state aid to local
governments in California was nearly 13 percent higher in 1999[51]
than in the last pre-Proposition 13 fiscal year (Table 11).[52]

Table
11

California
Local Government Property Tax and State Aid: Before and After Proposition 13

Year

Property
Tax

State
Aid

Total

Per
Capita

1978

$24,517

$23,048

$47,566

$2,083

1999

$21,582

$56,281

$77,863

$2,349

Change

12.8%

In 1999$

Source: Calculated from US
Census Bureau governments database.

Nonetheless, the loss of property taxing revenues resulted
in a search for other revenue increasing mechanisms. Local governments began to
implement fees on new developments for infrastructure, rather than the more
traditional general funds and bond revenues.

Development impact fees tend to be a flat rate established
by a local government, which is applied to a new house or a new rental unit,
rather than being related to the value of the property under construction. The
result is that the costs of new housing units are increased, and with a higher
percentage increase for lower cost units. Development impact fees are generally
applied to both single-family and multiple unit housing (Figure 4).

By 1999 average development impact fees averaged nearly
$25,000 per new subdivision house in California according to a study performed
for the California Business and Transportation and Housing Agency (Table 12).[53]
This represents $0.12 per $1.00 of construction valuation. On average,
development impact fees account for enough to permit the construction of an
additional house for each eight on which fees are assessed.

Throughout the regions studied, total fees ranged from a low
of $18,700 in the San Joaquin Valley to a high of $30,100 in the Central Coast.
But the fees can be much higher. In Watsonville, total fees were approximately
$60,000 per subdivision house, or $0.24 per $1.00of construction valuation. This is enough to permit an additional
house to be constructed for each four. Danville, not included in the state
survey, is reported to have a development impact fee of $64,320.[54]
This is barely 10 percent below the average price of a house in the least
expensive state, West Virginia (Table E-2).

Fees on infill single family housing were somewhat less,[55]
averaging $20,300, or $0.10 per $1.00 of construction valuation. The highest
average was in the San Francisco Bay area, at $26,800, while the low was in the
San Joaquin Valley, at $14,600. This means that fees account for enough to
permit the construction of an additional house per each ten.

The city of Brentwood (eastern Contra Costa County) had the
highest surveyed total fees in relation to construction value, at $0.28 per
$1.00. The development impact fees on four houses are enough to pay for
building a new house.

Impact on multiple unit construction: But the impact
is much more significant on multiple unit projects, as the situation in
California indicates (Table 13). The average per unit fees were more than 1.5
times the rate per $1.00 in construction value of single family homes, at $0.19
($15,500). The lowest per unit total fees were in the San Joaquin Valley, at
$10,900, at $0.18 per $1.00 in construction value. The Central Coast was
highest at $19,800,$0.24 per $1.00 in
construction value. Again, the city of Brentwood had the highest development
impact fee structure, at $41,200 per unit, or $0.62 per $1.00 in construction
value. Nearly two new units could be constructed with the fees from three units
built in Brentwood. California communities have some of the lowest multiple
unit vacancy rates, reflecting a shortage of supply. This is not surprising in
view of the exceedingly high development impact fees that are being used with
the effect of restricting construction of multiple unit housing. High
development impact fees on multiple unit construction are a material
contributor to the housing affordability crisis faced by low-income households
in the state.

Table
12

Development
Impact Fees in California by Region: Single Family Residences

Region

25
Unit Subdivision

Infill
House

Total

Fees

Fee per $1.00 Construction Value

Total
Fees

Fee
per $1.00 Construction Value

Northern California

$20,005

$0.114

$19,853

$0.106

San Francisco Bay Area

$28,526

$0.110

$26,819

$0.110

Sacramento

$27,480

$0.134

$21,834

$0.111

San Joaquin Valley

$18,728

$0.117

$14,631

$0.085

Central Coast

$30,061

$0.133

$19,448

$0.090

Southern California

$21,410

$0.106

$19,377

$0.094

State (Total Sample)

$24,325

$0.123

$20,327

$0.099

Source: Calculated from Landis, et al.

.

Table
13

Development
Impact Fees in California by Region:

Multiple
Unit Residences

Region

Fee
per $1.00 Construction Value

Fee
per $1.00 Construction Value

Northern California

$11,367

$0.165

San Francisco Bay Area

$18,428

$0.205

Sacramento

$15,793

$0.205

San Joaquin Valley

$10,929

$0.175

Central Coast

$19,784

$0.237

Southern California

$14,360

$0.197

State (Total Sample)

$15,531

$0.194

Source: Calculated from Landis, et al.

Impact on the Supplier market: The impact on the
supplier market is also significant. The California study found that the fees
added significantly to the initial cash requirements of developers. In Los
Angeles County, this amounted to an increase of 16 percent, while in Contra
Costa County the cash requirement was increased 53 percent.[56]
Such a requirement creates a significant financial burden on multi-unit
developers and can be expected to reduce the number of firms that can or will
compete in the market and the number of housing units produced.

Proffers: Development impact fees are not permitted
by the laws of some states. However, some jurisdictions have been able to use
“proffers,” contributions from developers for infrastructure in exchange for
project approvals.[57]
Proffers have the same general economic impact as development impact fees ---
they raise the price of housing and reduce affordability. Proffers are used
extensively, for example, in the northern Virginia jurisdictions of suburban
Washington, DC.

Development Impact Fees & Impact on Affordability: A
study by University of Chicago researchers[58]
found that development impact fees in the Chicago metropolitan area increased
the cost of both new and existing housing (Table 14).

Development
impact fees were estimated to increase the price of new housing by an
amount equal to from 63 percent to 212 percent of the amount of the fees.

Perhaps
more surprisingly, development impact fees were found to increase the cost
of older houses sold by an amount equal to from 63 percent to 171 percent
of the average development fee amount applied to new houses.

Development impact fees are lower in suburban Chicago than
in California,[59] though they
might have a similar financial impact there.

The University of Chicago researchers also found that
development impact fees induced homebuilders to build more higher cost housing,
to recover higher profit margins.

Table
14

Impact
Fees and

House
Prices:

Chicago
Suburbs

New
Houses

25
Year Old Resales

Average

99-130%

98-127%

High

212%

171%

Low

63%

63%

Source: Calculated from Braden
& Coursey.

Rationing Development: Development impact fees ration
the amount of housing that is constructed. It is not surprising that the
nation’s highest housing costs and some of the nation’s lowest rental unit
vacancy rates are in California, where development impact fees are used so
extensively. Moreover, some counties in the San Francisco Bay area are
rationing land through urban growth boundaries, which also raises the cost of
housing (below).

Impact on Low Income Affordability: Development
impact fees have a particularly negative effect on housing affordability for
low-income households:

Development
impact fees increase the cost of housing. This creates a burden for all
households, but more so for low-income households.

Development
impact fees are regressive. The fact that the same fee level is applied to
a house or rental unit being constructed has the inevitable impact of
burdening lower income households to a disproportionately greater degree.

As
administered in California, development impact fees are proportionately
higher on multiple unit construction, on which low-income households
especially rely.

Urban growth boundaries involve designation of land
available for urban development, simultaneously making urban development
outside the boundary illegal. The state of Oregon was the first to adopt this
strategy, having enacted legislation in the 1970s that requires virtually all
urban development to be within urban growth boundaries, established by
metropolitan agencies and cities. A number of other areas have more recently
adopted similar strategies, such as the states of Tennessee and Washington, the
Denver[60]
area, the Minneapolis-St. Paul area, the city of Austin[61]
and Contra Costa and Alameda Counties in the San Francisco Bay Area.

Land rationing raises prices: It is an established
principle of economics that rationing raises prices. Land is no exception. The
economic impact of urban growth boundaries, however, is not limited to the
impact on land prices. The principal mechanism for ensuring market prices is
competition. Where there is robust competition, the cost of goods and services
is generally less than where there is less competition. By designating which
land can be used for development, planning authorities reduce competition
between developers and land speculators. With less land to develop, owners of
land within the urban growth boundary can obtain higher prices. Both developers
and builders who are able to obtain developable land can charge higher prices
because there is no competition. Urban growth boundaries thus raise the costs
of virtually all factors of housing development.

Urban growth boundary legislation normally requires
inclusion of enough land to accommodate development needs for a period of time
(such as 20 years), but as the case of Portland (below) indicates, this is no
guarantee that a shortage of land will not occur, as bureaucracies impose visions
of greater density.

Potential for political manipulation: There is also a
potentially expensive and counter-productive political risk in land rationing.
The land development process becomes much more politicized, as developers and
landowners lobby regional land use agencies to include their properties, as
opposed to that of others in urban growth boundary expansions. This creates the
potential for inappropriate political contributions and other actions
(sometimes referred to as “political corruption,”) as the regional land use
agency is put in the role of “picking winners.”

Portland’s Urban Growth Boundary: Portland is by far
Oregon’s largest metropolitan area and is therefore the largest urban area in
the state with an urban growth boundary. Portland’s urban growth boundary, as
originally adopted in the late 1970s, included significant amounts of
developable land. As a result the urban growth boundary created little if any
shortage of land in the early years. Indeed, during the 1980s, even after adoption
of the urban growth boundary, the Portland urbanized area (developed area)
sprawled at a greater rate than all other major urban areas in the western
states.[62]

But in the 1990s, Metro, the metropolitan planning agency
responsible for the urban growth boundary, made a political decision that
Portland should become considerably more dense. Metro decided that, with higher
densities, there was enough land for 20 years of development within the urban
growth boundary little expanded from the late 1970s.[63]
But, as land was more severely rationed by Metro, development consumed much of
the land within the urban growth boundary, severe land rationing began to
occur. As a result housing prices in the Portland area escalated in an
unprecedented manner.

Portland: Housing Affordability Loss: It was
previously shown that the Portland area has had by far the largest reduction in
housing affordability of any major metropolitan areas over the past ten years.
The National Association of Homebuilders Housing Opportunity Index dropped 44.5
percent from 1991 to 2001, compared to an average 10.7 percent improvement.
Portland’s affordability loss was considerably greater than that of the second
worst performing market, San Francisco, at minus 27.2 percent (Table E-9).
Portland’s loss of productivity was well outside the range of the other major
markets. The gap between Portland and the market with the second worst loss in
affordability is greater than the gap between the second and 10th
worst affordability loss market. In 1991, Portland’s affordability was 16
percent above the national average. By 2001, Portland’s affordability had
slipped to 42 percent below the national average (Figure 5).

In addition, housing affordability declined sharply in
Oregon from 1990 to 2000, as noted above.

Oregon’s
average house value increased 74.6 percent (inflation adjusted) from 1980
to 1990. This is 18 percent more than Utah, which ranked second in house
value increase. Oregon’s increase was more than 3.5 times the national
rate (Table E-3)

Compared
to median house value, Oregon median household income declined 35.4
percent. As in Portland, the Oregon housing affordability loss was well
outside the performance range of other states and the District of
Columbia. Oregon’s 36.9 percent decline was nearly 25 percent greater than
that of second ranking Utah. The gap between 51st performing
Oregon and 50th performing Utah was more than the gap between
the second largest affordability loser (Utah) and the 7th
(Montana). Oregon’s loss in affordability by the income to house value
measure was more than four times the national rate (Table E-6).

In
1990, Oregon’s median income to house value ratio was 15 percent above the
national average. By 2000 Oregon’s ratio had fallen to 19 percent below
the national average (Figure 6).

San Francisco Bay Area: Similarly, the nation’s least
affordable housing market, the San Francisco Bay area, exhibits a similar
situation. While the more important factor there may be development impact fees
(above), urban growth boundaries have been adopted in Contra Costa and Alameda
Counties, two of the most urban counties in the area. The Contra Costa boundary
has been in effect for a decade.

Thus, at the same time that urban growth boundaries limit
development in the urban area, middle income and affordable housing may be
driven even further from the urban area. This is evident in the San Francisco
Bay Area, where much new middle-income housing has “leap frogged” to the San
Joaquin Valley, 50 to 80 miles from the urban area (such as the Stockton and
Modesto areas).

Impact on Low Income Households: Moreover, as was
noted above, this loss of housing affordability for potential homeowners has an
impact on rental markets as well. Generally, rents tend to rise with the cost
of single-family housing. This is already evident in the extremely high rents
in the San Francisco Bay Area, and can be expected to occur in other areas
implementing urban growth boundaries as time goes on. Because they rely more on
rental housing, and because they are more sensitive to housing cost increases,
low-income households sustain disproportionate costs from urban growth
boundaries.

Table
15

Housing
Affordability in Oregon Metropolitan Areas: 1991-2001

Metropolitan Area

1991:
2nd Quarter

2001:
2nd Quarter

Change

Eugene-Springfield

69.9

31.4

-55.1%

Portland

67.4

37.4

-44.5%

Salem

74.8

43.0

-42.5%

Medford (Note)

61.9

38.5

-37.8%

Note: Data for Medford is
1991, first quarter and 1998, 4th quarter (1991 and 2001 2nd
quarter data not available).

Source: National Association
of Home Builders Housing Opportunity Index data.

Land rationing through open space reservation can also
reduce housing affordability. Open space preservation has been among the most
popular smart growth strategies in public referenda. While open space
preservation can be a laudable objective, it generally encourages more urban
sprawl, not less.

“Leap-Frogging” in London: This is illustrated by
London, with its renowned “Green Belt.” This undeveloped ring of approximately
10 miles width around what is now the Greater London Authority (GLA) was set
aside from the 1930s to the 1950s. Since that time, the GLA population has
declined 1.5 million, while the population of counties bordering on the Green
Belt increased 3.5 million. Now, the London urbanized (developed) area is much
less compact than it would have been if adjacent development had been allowed
to continue. Development has “exploded” in large and small towns across nearly
3,000 square miles of southeast England. Total developed land is approximately
1,600 square miles.[64]
This has lengthened average commute trips and times. London’s Green Belt
may have created an aesthetically more pleasing urban area than if sprawl had
been allowed to consume the land uninterrupted. But the effect of London’s open
space preservation has been to “leap-frog” development to outside the Green
Belt, increasing, rather than containing urban sprawl.

Nonetheless, the impact of open space preservation is less
pervasive than urban growth boundaries, because open space preservation in
itself does not remove huge amounts of land from the potential for development.
As a result, open space preservation is generally less destructive of housing
affordability than urban growth boundaries.

Land Rationing and Home Ownership

The extent to which housing affordability has been eroded by
urban growth boundaries in Portland’s or elsewhere is unclear. But the
declining affordability trends are unmistakable. Moreover, they are consistent
with economic expectations under the circumstances --- prices have risen while
land has been rationed. Further the price increasing effect of Portland’s land
rationing may not yet be fully apparent. The longer term impact on home
ownership could be even more substantial.

If
one-half of the difference in Portland’s housing 10-year affordability
loss compared to that of Detroit or Milwaukee (the non-smart growth major
metropolitan areas with the largest affordability losses) is attributable
to land rationing, the eventual impact could be a five percent reduction
in home ownership. This would translate nationally into denial of home
ownership to more than 3.5 million households.[65]

If
one-half of the difference in Portland’s housing 10-year affordability
loss compared to the national rate is attributable to land rationing, the
eventual impact could be a 15 percent reduction in home ownership. This
would translate nationally into denial of home ownership to more than 10
million households.[66]

Consistent with economic theory, rationing land, especially
through the smart growth exclusionary planning strategy of urban growth
boundaries, increases housing costs and reduces affordability. Because lower
income households are more financially vulnerable, they shoulder a
disproportionately greater share of the burden.

Similar to the impact of exclusionary planning policies,
lesser degrees of sprawl are is associated with lower rates of home ownership.
According to Consumer Expenditure Survey data, home ownership tends to
be higher where sprawl is greater (density is lower). Using the urban sprawl
classifications developed by the Surface Transportation Policy Project (STPP),[67]
the most sprawling urban areas average 70 percent home ownership, compared to
only 57 percent in the least sprawling areas (Table 16).[68]

Because minority households generally tend to have lower
incomes, home ownership rates are lower on average. Smart growth’s exclusionary
planning can therefore be expected to more negatively impact minority
households, because it artificially increases housing costs. This is consistent
with findings from a recent study by Matthew Kahn of Tufts University, which
found that Black home ownership tends to be higher and Black household dwelling
size is larger where there is more sprawl.[69]
In the report, Kahn indicated:

Affordability is likely to decrease in the presence
of more antisprawl legislation.

As was noted above, rents tend to be higher where house
values are higher. Thus, as smart growth raises housing costs, it not only
makes it more difficult for lower income households to achieve home ownership,
but it also is associated with higher rental payments. This has the potential
to increase both the number of eligible recipient households and costs per
housing assistance recipient, which can work to reduce the number of households
that can be assisted.

3.25 SMART GROWTH
AND THE COST OF LIVING

Similarly, the costs of housing tend to be higher in areas
that sprawl less. Again, using the STPP sprawl classifications and Consumer
Expenditure Survey data, expenditures for shelter tend to be lower in
metropolitan areas that sprawl more. Expenditures for shelter in the least
sprawling urban areas were 36 percent higher than in the most sprawling urban
areas. The difference in housing expenditures more than compensates for the
expected higher transportation expenditures.

Further, food costs were similarly higher where sprawl was
the least. Overall, transportation, shelter and food expenditures in the least
sprawling areas were 13.6 percent higher than in the least sprawling areas. It
thus seems likely that overall transportation, housing and food costs for
low-income households is less where sprawl is greater (Table 17). The higher
overall costs may be the result of various factors, such as higher land prices
in more dense areas, higher costs of doing business, higher costs of doing
business due to greater traffic congestion and less competitive markets.

Higher overall costs of living particularly burden
low-income households, many of which are eligible for housing assistance.
Moreover, higher the higher housing expenditures can increase the cost of
housing programs, further rationing the number of households that can be
assisted.

Lower overall household expenditures are associated with
metropolitan areas that sprawl more, which benefits all income classes and
makes it possible to serve more households with housing assistance.

3.26 ELIGIBLE RECIPIENT
TRANSPORTATION:
SITUATION

The achievement of higher population densities is a
necessary, though not sufficient requirement for achieving the objectives of
smart growth. The expected transportation related benefits of smart growth,
such as reduced traffic congestion, reduced air pollution and reduced journey
times, would therefore seem to be generally evident in more dense urban areas

In fact, however, most measures indicate that the higher
densities that smart growth would bring are associated with a lower standard of
living and higher cost of living. As a result, smart growth increases the
burden of low-income households, including those eligible for housing
assistance.

Traffic and Density: Traffic congestion is less
intense where densities are lower. This perhaps counterintuitive situation
results from a misunderstanding of the dynamics of traffic congestion and urban
densities. It has often been suggested that urban sprawl is associated high
higher levels of traffic. However, the very spreading out of the urban area
that occurs with sprawl has the tendency to reduce, rather than increase
traffic congestion. US measures tend to indicate lesser levels of traffic
congestion in the less dense (more sprawling) urban areas (Figure 7). Gordon
and Richardson have suggested that urban sprawl, with its lower densities, has
been the safety valve that has kept US traffic manageable.[70]
Similarly, traffic congestion tends to be even worse in the more dense
international urban areas (Figure 8). Federal Highway Administration research
indicates that, at average US urban densities, the number of vehicle miles
traveled tends to rise at a rate of 0.8 percent to 0.9 percent for each 1.0
percent of increase in density.[71]
This means, for example, that if an urban area were to double in population
density the vehicle miles traveled per square mile would increase by from 80
percent to 90 percent (Figure 9).

Traffic Speed and Density Further, as traffic density
increases, speeds decline, further exacerbating density’s negative impact. For
example, with their higher population densities, European urban areas tend to
have traffic intensities double that of US urban areas. When the slower speeds that
result from the greater traffic congestion are factored in, the time (vehicle
hours) spent driving per square mile is more that 3.5 times that of US urban
areas (Figure 10).

Air Pollution and Density: Moreover, air pollution
generally tends to be associated with lower operating speeds and the “stop and
go” operating conditions associated with traffic congestion. The higher
operating speeds achieved in the less dense urban areas contributes to lower
levels of pollution intensity (Figure 11). In the United States, automobile air
pollution production is the least at constant speeds of 35 miles per hour to 55
miles per hour.[72]The faster speeds that are typical in the
United States, combined with the lower traffic densities result in less intense
air pollution than in international urban areas that are more dense (Figures 12
through 14).[73] Moreover,
air pollution intensity is lower in US urban areas that have lower population
densities --- the areas that sprawl more (Figure 15).[74]
Finally, contrary to popular perception, gross air pollution production by
automobiles has declined over the past three decades, at the same time that
driving has increased more than 30 percent and urbanization areas has sprawled
more than 100 percent[75]
(Table 16).

Auto and Transit Speeds: Despite perceptions to the
contrary, transit is considerably slower than the automobile. Generally, in the
United States, average automobile commute time by automobile was reported by
the Nationwide Personal Transportation Survey to be 20.1 minutes in 1995, less
than one-half the transit figure of 48.7 minutes (Table 18). Average automobile
commute speeds are 35.3 miles per hour, compared to 15.3 miles per hour for
transit (including waiting time).[76]Indeed, the United States Department of
Transportation has noted that improvements in average commute travel speeds are
partially the result of:

As was noted above, Portland, Oregon has implemented the
nation’s most aggressive land use regulations (smart growth), Portland has
opened two light rail lines and has significantly increased overall transit
service levels. According to the Texas Transportation Institute, Portland’s per
capita traffic volumes increased more than that of any other urban area with
more than 1,000,000 population.[78]
In spite of its smart growth policies, Portland’s traffic congestion increased
markedly from 1990 to 1999, and now ranks 8th in the nation, with a higher
Travel Time Index[79]
(congestion index) higher than Atlanta, which is renown for its traffic
congestion.[80] Yet, automobile
commute times remain approximately one-half that of transit.[81]

In addition, commutes of one hour or more remain
comparatively infrequent in the US, though increasing. The 2000 Census
Supplemental Survey indicates that 7.3 percent of commuters traveled one hour
or more to work. A much higher percentage of transit trips, 33.6 percent, were
one hour or more. By comparison, 6.1 percent of trips by other modes
(principally automobile) were one hour or more Table 19).

Similarly, transit’s share of total work trips rises as
travel time increases. While transit’s share of work trips is 5.2 percent
nationally, its share of work trips one hour or more is 24.6 percent, nearly
five times as high. Again, even in Portland, where smart growth strategies have
been implemented with the most comprehensiveness, the one-hour and longer
category represent has a transit work trip market share nearly five times that
of the area in general (Table 20).[82]

Journey to Work: Lower density (more sprawl) is
associated with shorter, rather than longer commute times. In 1990, workers in
the most dense US urban areas spent nearly one-quarter more time commuting than
those in the lowest density urban areas (Table 21), or 40 additional hours
annually.

The same situation exists in international urban areas. One
of the frequently cited objectives of some growth is to replicate the more
dense European city form. In fact, the data indicates that, on an international
basis, longer journey to work times are also associated with higher density,
not lower density urban areas. The most dense urban areas tend to have average
commute times 45 percent longer, with commuters spending 76.6 hours more
traveling to work than those who live in the least dense urban areas (Table
22).[83]

This is evident in a comparison of individual urbanized
areas. Shorter journey to work travel times tend to be associated not only with
lower density, but also with lower public transit market shares (higher
automobile market shares). For example, Stockholm, often cited as a model of
urban effective planning, has an average commute time of 32.2 minutes. Phoenix,
which is especially illustrative of urban sprawl (low density and little
concentration of employment, with a comparatively small downtown area) has an
average commute travel time of 22.9 minutes. The average commuter in Phoenix
spends approximately 80 hours less each year traveling to work as in Stockholm,
despite the fact that Phoenix has one-third more population and an urbanized
land area nearly five times as large (Table 23).

Low Income Household Commute Times: Low-income
households[84] benefit
from the faster journey times characteristic of America’s low-density urban
areas. Despite the fact that low-income commuters tend to rely on slower
transit services disproportionately, their journey to work profile is similar
to that of the whole (Table 24):[85]

5.2
percent of workers in poverty households travel one hour or more to work,
compared to the overall figure of 4.6 percent.

Average
travel distances and travel times are less for workers in poverty
households than that of all workers.

The perception that increased reliance on the automobile has
increased commute times, whether for all of the population or simply low income
households, is inconsistent with reality. Where transit systems are more
heavily used, work trip travel times are longer, whether in the United States
or elsewhere, because transit generally operates at slower speeds than
automobiles.

Impact on Housing Assistance: Because smart growth is
associated with greater levels of traffic congestion, more intense air
pollution and longer commutes, it has the potential to retard the quality of
life for all, including households that are eligible to receive housing
assistance. Moreover, to the extent that higher densities increase travel
times, it is possible that employment will be reduced. To the extent that this
occurs among low-income households, a greater financial burden could be placed
upon housing assistance programs.

Finding: Smart growth is associated with greater
traffic congestion, longer commute times and more intense air pollution.

3.27 ELIGIBLE RECIPIENT TRANSPORTATION: PROSPECTS

“Transit Choice” and Auto-Competitive Transit

Entire urban areas are labor markets, especially for people
who have access to cars. Smart growth seeks to provide alternatives to the
automobile, through what is referred to as “transit choice,” which would make
more auto-competitive transit service available.

But, it is difficult, if not impossible to provide transit
choice for all but a few. The principal difficulty with transit choice is that
it is not possible, within reasonable financial constraints, to provide transit
service that is competitive with the automobile throughout modern urban areas
(auto-competitive service).[86]
Transit’s slower speeds severely limit the geographical market for jobs
available to users. Generally, the geographical labor market area available to
automobile users is 5.3 times that available to transit users. For example
(Table 25):[87]

·In 20 minutes, the average automobile commuter can
access a theoretical labor market[88]
of 434 square miles, compared to 82 square miles for transit. According to
Federal Highway Administration estimates, 43 percent of the urbanized
population of the United States is in areas smaller than the automobile’s
20-minute labor market, compared to 10 percent for transit (Figure 17).[89]

·In 40 minutes, the average automobile commuter can
access a labor market of 1,736 square miles, compared to 327 square miles for
transit. Approximately 77 percent of the nation’s urbanized population lives in
areas smaller than the automobile 40-minute market, compared to 34 percent for
transit. The 40-minute automobile market is larger than all urbanized areas
except for New York, Chicago, Los Angeles and Atlanta. At 1,757 square miles,
Atlanta is only marginally larger than the 40-minute theoretical labor market.

·In one hour, the average automobile commuter can access
a labor market of 3,902 square miles, compared to 735 square miles for transit.
More than 90 percent of the nation’s urbanized population lives in areas
smaller than the automobile 60-minute market, compared to 55 percent for
transit.

·Only New York, at 3,962 square miles, covers more land
area than the 60-minute automobile commute labor market.[90]

There is overall economic justification for access to larger
labor markets as opposed to smaller ones. International research indicates that
the productivity of urban areas increases 2.4 percent for every 10 percent
increase in labor market size.[91]

Walkability, Transit-Oriented and Mixed-Use Development

Smart growth seeks to solve transportation problems by
improving the spatial relationship between jobs and residences. The theory is
that by proper siting of major facilities and by encouraging development along
high capacity transit lines, demand can be focused in such a way that
automobile use can be reduced, while transit and walking (“walkability”) are
encouraged. There is also the view that traffic congestion can be reduced by
improving the jobs-housing balance through mixed-use developments (transit
oriented developments) that incorporate both residential and commercial uses.

Generally, however no-one, including urban planners
architects, economists or others, can reliably anticipate people’s preferences
with respect to home and work location. Some people make a conscious choice to
have larger yards and larger houses in exchange for a longer commute. Others
are willing to accept smaller lots and accommodations to be closer to work.
People change jobs more frequently now than in the past, while a large
percentage of households have more than one wage earner, which can make it more
difficult to minimize work to employment trip lengths. In short, while
minimizing trip distance may be an objective of transportation planners and
urban planners, it is often not a principal objective of households. Throughout
history, people have, by their conduct, considered entire urban areas to be
their effective labor markets. While the average work trip has long been in the
range of 20 to 25 minutes, there have been people who choose to commute much
longer periods of time.

The same is true of shopping trips. People do not
necessarily shop at the nearest store. Stores located in more remote areas may
seek to encourage people to travel longer distances by lower prices or other
incentives.

The Reality: Whatever the merits of mixed-use
development, walkability or transit-oriented development, the potential of
these strategies to make a significant difference in transportation demand is
severely limited. For example, in Portland, which has constructed a number of
transit oriented developments, the share of people walking to work declined nearly
30 percent from 1990 to 2000.[92]
Further, Peter Hall has shown that Stockholm’s best efforts to transform
transport by improving the jobs-housing balance, with its new towns, has done
little to attract people to work in their own neighborhoods, despite the
comparatively large number of jobs within walking distance.[93]
The Stockholm experience is particularly instructive, since the city government
owned most of the land that was used for development, and so had much greater
design control than would have been the case if it had been forced to seek its
planning objectives through a private development market as in the United
States.[94]

Threat to Low Income Households: Further, the impetus
to build transit oriented and walkable communities could work to the disadvantage
of households eligible for housing assistance. In a number of US central cities
there is considerable new development and redevelopment of older housing stock
and conversion of commercial buildings into housing (called “infill” or
“gentrification”). Often these developments are publicly subsidized, either
directly or through tax abatements.

These developments tend to target upper and middle-income
households. It is to be expected that such developments will tend to displace
lower income households, which are now disproportionately concentrated in the
same areas. It could be more difficult, if not impossible, for former inner
city low income households who have been displaced by higher income households
to reach travel destinations by transit, because transit service is less
readily available in the inner-suburban areas to which they are likely to be
forced to move.

Compartmentalization: Mixed-use development,
walkability and transit-oriented development appear to represent an attempt to
compartmentalize modern metropolitan areas. By recreating faux-small town
environments with homes, employment and shopping, it is hoped that people will
do more of their travel in the immediate local area, and less throughout the
rest of the urban area. This view is at odds with the very locational economics
that justify urban areas in the first place. Large urban areas exist, at least
in part, because of the scale economies that arise from having large labor and
consumer markets within reach of large employment and shopping markets. The
larger, more remote “big-box” stores are able to provide goods and services at
lower prices than the small neighborhood stores that are likely to locate in
compartmentalized, walkable areas. It is to be expected that people will drive
by closer stores that are more expensive so that they can stretch the value
obtained for their limited resources. While overall traffic levels increase,
these less expensive, more remote stores improve the quality of life and make
people more affluent than they would otherwise be.

The residents of walkable areas may work at virtually any
location throughout the urban area. Often, the businesses that locate in
walkable neighborhoods employ lower wage-rate service workers, while the
residents have much higher incomes than could be earned at the local
businesses. Achieving a “jobs-housing” balance may be possible from a
theoretical numeric perspective, but the ultimate jobs-housing balance is
obtained in the overall labor market, which increased mobility expands to cover
most, if not all of the urban area.

Bringing Jobs and Shopping to the People? The hope
that modern urban areas can be redeveloped to better match jobs and residences,
leading to a fundamental change in travel patterns, is unrealistic. Even if
there were a broad commitment to the required and significant land use changes,
the conversion process would take at least as many decades as the current urban
form has taken to develop. Even Portland, with its aggressive smart growth
policies, does not anticipate achieving Los Angeles densities (much less the
much higher density European or Asian urban areas) in 50 years (Appendix D).
Indeed, no urban redesign vision has been seriously proposed that would achieve
smart growth’s objectives at a metropolitan level. Such visions have been
limited to localized, ad hoc plans. Portland’s 50-year plan calls for a modest
decline of six percent in automobile market share.[95]
Similarly, long-range transportation plans project little comparative increase
of automobile demand to transit, despite substantial investments in transit.[96]

The
political and economic reality is that there is no prospect for redesigning
urban areas in a manner that materially improves employment mobility
opportunities for eligible recipients assistance in the near future, if ever.
And, given the superior performance of the transportation system in US urban
areas relative to urban areas in other high-income nations, there seems to be
no imperative to do so. There are simply no functioning models that perform
better.

Thus,
walkability, to the extent that it seeks to reform the city by bringing
shopping and employment in proximity to residences, is likely to have
transportation impacts only on the margin. The principal reason is that people
make local travel decisions involving many more factors than travel time or
travel distance. So long as people are not inclined to work at the closest job
or shop at the closest store, it will make little sense to try to “bring” jobs
and shopping to them through walkable, transit-oriented or mixed-use
developments. This is not to suggest that walkable, transit-oriented or
mixed-use developments should not be built. It is only to note the
transportation demand changing limitations of such strategies.

Table
25

Theoretical
Labor Market Size: Automobile & Transit

Time

Automobile:
Square Miles

%
of Urbanized Population

Transit:
Square Miles

%
of Urbanized Population

00:20

434

43%

82

10%

00:40

1,736

77%

327

34%

01:00

3,906

90%

735

55%

01:20

6,944

100%

1,307

72%

Source: Calculated using the
average commute speeds reported by the Nationwide Personal Transportation
Survey, 1995.

Expanding Labor Markets for Low Income
Households: Employment is a crucial element in improving the economic
status of low-income households. Consumer Expenditure Survey data
indicates that the worker-to-household ratio is a 28 percent lower among lowest
income quintile households than others. (adjusted to exclude children and
senior citizens).[97]

In recent decades, employment has become far more dispersed
throughout the continually expanding urban area. Employment opportunities are
likely to be maximized if potential workers are able to access most or all of
the geographical labor market that exists in an urban area. Low-income
households have less access to automobiles and often, therefore, find it
difficult to reach jobs that are far away or not easily accessible by transit.

In 1999, 66 percent of lowest income quintile households
owned cars, compared to the average of 94 percent for the other four quintiles.
Thus, low-income households without automobiles tend to have much smaller labor
markets from which to choose than other households. However, progress is being
made, with automobile ownership rising 6.5 percent in the lowest income
quintile over the past 10 years (Table 26).But at this rate, it would take more than 50 years to bring average vehicle
ownership among low-income households to the level of the rest of the
population.

Table
26

Automobile
Availability: Lowest Income Quintile

Year

Vehicle
Availability

1989

62%

1994

62%

1999

66%

Change 1989-1999

6.5%

Source: BLS Consumer Expenditures Survey.

Commuting to the New Jobs: As urban areas have become
more dispersed in residential locations, jobs have moved as well. As a result,
the average downtown area (central business district) represents barely 10
percent of a metropolitan area’s employment.[98]
Public transit systems most effectively serve downtown areas,[99]
but tend to provide little effective service to job locations in other areas.
For example:

·In metropolitan Boston, with one of the nation’s most
comprehensive public transit systems, only 32 percent of employers are located
within walking distance (¼ mile) of transit.[100]
While 98 percent of Boston’s inner city low-income households are within ¼ mile
of transit, they are largely unable to reach the large majority of employers
located in suburban areas. Virtually no suburban jobs in high growth areas can
be reached from Boston by a 30-minute transit commute, and only 14 percent can
be reached within one hour.[101]
The situation is even more stark for low-income households living in the
suburbs and working in other suburbs. Most trips require a transfer in central
Boston and would take even longer than the central city to suburban employment
trips described before.

·In Atlanta, only 34 percent of metropolitan jobs are
within on hour’s transit commute for low-income households.[102]
The Atlanta area is massively reorienting its transport investment away from
highways and toward transit. Yet, after investing 55 percent of all
transportation resources in public transit improvements over the next 25 years,
it is projected that only 39 percent of metropolitan jobs will be within one
hour’s transit commute for low income residents in 2025.[103]

·In Portland, which has adopted the nation’s most
aggressive growth management policies and has expanded transit service
significantly, it is estimated that only four percent of residences are within
a transit commute of non-downtown jobs that requires 1.5 times the automobile
commute. Non-downtown jobs are accessible to 24 percent of residences for
commutes that are double the automobile travel time Appendix F). This creates
substantial burdens for low-income workers who do not have access to autos.
And, despite what might be termed the best of intentions, the situation is
expected to worsen. Over the next 20 years, despite a further significant
planned increase in transit service, Portland’s regional planning agency
indicates that a smaller percentage of jobs (from 86 percent to 84 percent) and
a smaller percentage of residences (from 78 percent to 73 percent) will be
within walking distance of transit service.[104]

·In Dallas, low-income commuters to non-downtown
locations can be faced with round trip travel times of up to four hours daily
(Appendix F). Many jobs are simply not available by transit, regardless of
travel time.

The growing complexity of urban travel patterns further
detracts from transit’s competitiveness. Transit is often impractical for
people making “segmented” trips --- such as work trips that include more than
one purpose, such as shopping or trips to child care centers. The single-parent
nature of many low-income households results in more segmented trips.

Transit’s Downtown Orientation: The basic problem is
that transit, despite its unique ability to serve concentrated[105]
markets such as downtown is not well positioned to serve what has emerged as
the dominant commuting pattern --- dispersed suburban markets. This is
illustrated by the fact that US suburban employment centers (of which some are
now larger than downtown areas) has such limited public transit work trip
market shares, often five percent or less.[106]

Public transit work trip market shares are small outside
downtown areas because little auto-competitive transit service is provided.
This is illustrated by examining household income levels by commute sector
(Figure 18).[107]

The
“Choice” Market: Downtown: Commuters to downtown areas have household
incomes that are 92 percent of average incomes, and 80 percent above the
poverty threshold for three person households. Because their incomes are
similar to that of the metropolitan average, it is reasonable to assume
that the average downtown commuter has automobile availability similar to
that of the population in general. This means that, to use the transit
marketing parlance, downtown transit commuters are a “choice”” market ---
people who have the choice of using transit or their cars.

The
“Captive Market:” Outside Downtown: By contrast, commuters to areas
other than downtown have much lower incomes, at only 59 percent of average
(Table E-13). The average non-downtown commuter has a household income
just 15 percent above the poverty threshold. Among 32 urban areas with
large downtowns, non-downtown commuter income was below the poverty
threshold in 13. As is noted above, lower income households have lower
levels of automobile availability. For the most part, it appears that
non-downtown transit commuters are a “captive” market for transit.

The Limits of “Transit Choice:” To provide a region-wide
system that provides transit choice for all trips would be prohibitively
expensive. Indeed, even in international urban areas with far more
comprehensive transit systems, most trips that do not begin or end in the
central area cannot be completed in a reasonable amount of time by transit.
Like residents of Phoenix, suburban Parisians tend to commute to suburban jobs
by car, because transit is either unavailable or takes too long (Appendix D).
It has been estimated that the cost to provide automobile competitive transit
choice throughout a US metropolitan area of 1.2 million population would cost
from 70 to 350 times the present level of transit expenditure in major
metropolitan areas.[108]
This would require the equivalent of from 20 percent to more than 100 percent
of the annual personal income of the area. Obviously, even at the lower found,
such a financial commitment is virtually beyond comprehension. Thus, like
affordable housing programs intended to compensate for housing cost increases,
the objective of widespread transit choice is simply out of reach.

Expanding Employment Opportunity with Automobiles: The
most immediate, effective and inexpensive effective strategy for improving
mobility and access for low-income households, including households eligible
for housing recipients is to make automobiles available. Consistent with this,
President Clinton issued an executive order in 2000 that made it easier for
welfare recipient households to obtain automobiles.[109]
The alternatives are simply too costly.

Genuine
transit choice cannot be afforded within the constraints of the present
low-density urban form, as noted above.

The
changes in urban form that would be required are so draconian as to be
impossible. Even in European urban areas, which are much more dense and
have more dense urban forms, genuine transit choice cannot be provided
except in comparatively small areas (Appendix D).[110]

Low-income households are most likely to achieve their
employment potential if their geographical labor market is larger, rather than
smaller. The automobile generally provides access to the largest possible labor
market. Thus, it makes more sense to facilitate movement of people (low-income
and otherwise) to shopping and employment throughout the urban area, than to
expect that changes to the urban form can bring shopping and employment closer
to where they live.

Smart growth’s exclusionary planning has a significant
impact on households that are eligible for housing assistance. As exclusionary
planning raises housing prices and limits supply, fewer households are able to
afford the housing they require, and the number of eligible recipients
increases. These inevitable housing cost increases increase the demand for
housing assistance by increasing the number of eligible recipients. At the same
time, the housing cost increases reduce the effective supply of housing assistance
by increasing the cost of subsidizing individual households.[111]

Smart growth seeks to curb urban sprawl, which is associated
with higher home ownership rates, lower costs of living, and reduced travel
times. Moreover, smart growth seeks to discourage automobile use, despite the
fact that the automobile makes it possible to access much larger expanses of
the urban area. Each of these impacts of densification and smart growth works
against incorporating low-income households, including eligible recipients of
housing assistance, into the economic mainstream. As a result, through these
impacts smart growth increases the financial burden of housing assistance
programs, which are already rationing assistance.

It might be suggested that the cost increasing impacts of
smart growth and exclusionary zoning can be neutralized by government mandates
or subsidies to expand affordable housing. It is possible to provide assistance
for some (a small percentage) of those harmed by exclusionary planning. But
necessarily, politics and public budgets constraints render such programs far
too small to mitigate the harm done to low-income households, much less that
imposed upon the much larger number of households across the income spectrum.

Exclusionary planning raises the cost of virtually all
housing, creating an overwhelming potential public financial burden. To negate
the cost raising impact of smart growth would require subsidizing a very large
number of, if not most households.

There is no reason to believe that the nation or its
communities will undertake a massive subsidy program to negate the impacts of
exclusionary planning. No community that has adopted smart growth’s
exclusionary planning has implemented a comprehensive program to negate cost
increase impacts on more than an “ad hoc” basis.

As noted above, current expenditure levels are insufficient
to provide for all eligible recipients. Indeed, housing assistance itself is
being rationed to as little as one-third of the eligible recipients. Moreover,
the nation has not and is not likely in the future to provide the level of
housing assistance to support currently eligible recipients of housing
assistance. The anticipation, therefore, that sufficiently funded affordability
subsidy programs can be established to mitigate the financial damage imposed by
smart growth’s exclusionary planning, which will injure a much larger
population, is without foundation.

Assessment:Policies that raise the cost of housing will deny adequate housing to
some.

At
any given level of public expenditure, such policies must reduce the
number of households for which housing assistance can be afforded.

As
smart growth’s exclusionary planning raises the cost of housing, fewer
households will be able to afford their own homes.

Widespread adoption of exclusionary planning (smart growth)
is likely to reduce home ownership levels and could reverse the substantial
progress toward the national goal of greater home ownership. This burden will
fall most on lower income households, which are disproportionately minorities.
Thus, an indirect impact of exclusionary planning could be to reverse progress
toward another national goal, integrating minority households into the economic
mainstream. Present home ownership levels and progress toward social and
economic inclusion are not likely to be sustainable in an environment of smart
growth’s exclusionary planning.

In the final analysis, the inevitable affordability
destroying impacts of exclusionary zoning and smart growth’s exclusionary
planning are at their very root inconsistent with policies that would seek to
ensure adequate shelter for all.

Finding: Because it is not feasible to negate its
affordability destroying impacts, smart growth works at cross-purposes to the
nation’s housing assistance programs.

3.29
SMART GROWTH AND AFFORDABILITY:
ASSESSMENT

Providing a sufficient supply of competitively priced
housing is a prerequisite to housing affordability. While considerable research
has been conducted on the economic impact of regulatory barriers, it is useful
to recall a fundamental dynamic of economics --- that, all things being equal,
policies that ration (create shortages) raise prices. Excessive regulation,
discouraging economic activity (such as development) and rationing factors of production
(such as land) all create shortages. Policies that systematically create
shortages in the housing market must have the eventual, if not immediate impact
of reducing affordability.

Alternative theories may be postulated. For example, it has
been suggested that Portland’s housing affordability difficulties are due to
excess demand created by population and economic growth. However, the nation’s
fastest growing metropolitan areas, both in terms of population and economics,
have not adopted smart growth and have not suffered similar housing
affordability losses (Appendix C). In the longer run, the well-documented
tendencies of prices to rise where there is rationing seems likely to prevail.

While the rationale for smart growth’s exclusionary planning
policies may be more innocent than those of the older exclusionary zoning
policies, the impact on low-income households is virtually the same. Whether
driven by elitism or prejudice, as in the case of exclusionary zoning, or
disregard of economics, as in the case of smart growth, the result is the same
--- low-income households are denied housing opportunity.

This is not to endorse urban sprawl or low-density
development per se. It is simply to note that, however unattractive, urban
sprawl is generally associated with a higher quality of life for low-income
households.

A Worst Case Scenario: It is often not recognized
that the modern American urban area is the result of urban planning. For more
than 50 years, American urban areas have been shaped by zoning, which has
separated land uses and may have forced urban densities lower than they would
otherwise be.[112] Smart
growth seeks to correct or stem the abuses of zoning by the imposition of new
regulations. This could be a mistake.

Smart growth’s exclusionary planning (and its cousin,
exclusionary zoning); substitute the judgment of planners and the political
process for that of households and those who develop both residential and
commercial projects. Neither planners nor politicians can reliably predict or replicate
the preferences of consumers. Further, planning and politics have not generally
been successful in changing the preferences of people.[113]

In the longer run, it can be expected that smart growth’s
exclusionary planning, like exclusionary zoning, will bring its own
distortions, as consumers seek their preferences that do not conform to the
policies of the planners. Geographical areas outside urban growth boundaries
and smart growth regulation could grow faster, accelerating sprawl, following
the pattern of growth that occurred in response to London’s Green Belt. In the
short term this would lead to longer automobile commute trips. In the longer
term, this would lead to even lower urban densities (greater urban or even
rural sprawl) and more dispersed employment locations, as new commercial areas
are established to serve new, more remote residential development. It is not
inconceivable that remote informal housing developments (perhaps even
“shantytowns”) could arise, with low income households that would otherwise
have located in less expensive suburban single family dwellings instead
locating in substandard homes on tracts of land outside regulated areas.[114]
This too would increase sprawl and increase automobile commuting distances.

Two Metropolitan Tiers? There is the potential for
the development of a two-tiered metropolitan system in the United States. Some
metropolitan areas will opt for smart growth and emerge in a top, elite tier.
Generally, entry into housing markets in these areas will require higher
income, while existing low income households already in the area could be
gradually forced out of the area. This may already be evident in the San
Francisco Bay Area and to a lesser extent in the Boston[115]
and Portland areas. Meanwhile, middle-income movers and low-income households
would be increasingly concentrated in the inclusionary metropolitan areas that
do not adopt smart growth’s exclusionary planning.

Compensating Benefits? It might be argued
that the consequences of smart growth’s exclusionary planning would be
acceptable if there were more than compensating benefits. But smart growth does
not appear to produce benefits that negate its attributable destruction of
housing affordability. For example, where there is less sprawl (where urban development
is more consistent with smart growth policies):

Home
ownership rates are lower.

Low-income
household home ownership rates are lower.

Black
home ownership rates are disproportionately lower.

Cost
of living expenditures are higher.

Work
trips take longer

Traffic
congestion is greater

Air
pollution is more intense

These are not factors that
improve the quality of life, whether for the population in general or eligible
recipients of housing assistance in particular. The rapid adoption of smart growth,
because of its inconsistency with economic dynamics, is likely to significantly
reduce housing affordability.

Based upon the analysis above, the following policy options
are suggested to encourage improved housing affordability:

Income Estimation:

·The U.S. Department of Commerce, the U.S. Department of
Labor and the U.S. Department of Housing and Urban Development could establish
a process for determining the cause of these disparate estimates and propose
methods by which accurate and consistent data can be developed and routinely
reported by both reporting systems.

·Once the more accurate system is in place, US
Department of Housing and Urban Development could prepare an estimate of the
number of households eligible for housing assistance.

The
Secretary of Housing and Urban Development could recommend to the
President the issuance of an executive order reaffirming the fundamental
commitment of the U.S. Government to continued home ownership expansion
and housing opportunities for all. The order could review the progress
toward increasing home ownership among the population in general and with
respect to minorities in particular. The executive order should, within
the constraints of applicable law, forbid the use federal funding by federal
departments and agencies for programs that promote smart growth policies
that would ration land or development (such as urban growth boundaries or
development impact fees) and are thereby likely to reduce housing
affordability.

The
U.S. Department of Housing and Urban Development could publish an Urban
Development and Housing Affordability Guide Book for local communities on
the negative impacts of regulatory barriers to housing affordability, with
particular emphasis on the impacts of exclusionary zoning and smart
growth’s exclusionary planning policies. The Urban Development and Housing
Affordability Guide Book could include information with respect to the
quality of life impacts of smart growth policies for eligible recipients
of housing assistance.

The
U.S. Department of Housing and Urban Development could prohibit the use of
research and technical assistance funding for the support of projects and
programs that contribute to the problem of housing affordability, such as
exclusionary zoning, and exclusionary planning (land rationing and
development impact fees)

The
U.S. Department of Housing and Urban Development could establish and
maintain a comprehensive, locality specific database of regulatory
barriers such as urban growth boundaries, other land rationing
initiatives, development impact fees (including amounts) and any other
such provisions inconsistent with the established economic principle that
rationing leads to higher prices and reduced housing affordability. Once
such a database is developed, the US Department of Housing and Urban
Development could produce an annual report on progress toward removing
regulatory barriers to affordability and develop policy options (actual
federal and models for states and localities) to encourage removal of barriers
to affordability.

During the 1990s, more than 40 percent of
the nation’s population growth was accounted for by immigration.[116]
Because immigrants typically have lower household income levels than average,
it is likely that, where their composition of growth is higher, greater
pressure will be placed upon the rental markets on which eligible recipients of
housing assistance tend to rely. While detailed local and metropolitan
information is not yet available from the 2000 Census, immigration was
particularly intense in some of the states that have the lowest rental vacancy
rates. For example (Table A-1):

Immigration
accounted for 154 percent of growth in New York, 100 percent in
Connecticut and 89 percent in New Jersey. New York and New Jersey were
ranked with the 6th and 7th lowest rental vacancy
rates in 2000, while Connecticut ranked 11th.

Immigration
accounted for 80 percent of California’s growth from 1990 to 2000.
California had the third lowest vacancy rate the nation.

Immigration
accounted for 95 percent of growth in Massachusetts from 1990 to 2000.
Nearby states, which have received peripheral Boston metropolitan growth
ranked 1st and 8th lowest in vacancy rate (New
Hampshire and Rhode Island).[117]
California had the third lowest vacancy rate the nation.